A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer

Cenzhu Wang; Kun Xu; Fei Deng; Yiqiu Liu; Jinyi Huang; Runtian Wang; Xiaoxiang Guan

doi:10.21037/tcr-20-3471

A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer

Transl Cancer Res. 2021 May;10(5):2229-2246. doi: 10.21037/tcr-20-3471.

Authors

Cenzhu Wang^#¹, Kun Xu^#¹, Fei Deng², Yiqiu Liu¹, Jinyi Huang¹, Runtian Wang¹, Xiaoxiang Guan¹

Affiliations

¹ Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
² Department of General Surgery, Pukou Branch of Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

^# Contributed equally.

Abstract

Background: Breast cancer (BC) is one of the most common cancers worldwide and patients with lymph node metastasis always suffer from a worse prognosis. Tumor mutation burden (TMB) has been reported as a potential predictor for tumor behaviors. However, the correlation between TMB and lymph node metastasis of BC remains unclear. This study aimed to explore TMB-related biomarkers to predict the lymph node metastasis in BC patients.

Methods: A total of 949 BC patients with RNA-seq data, mutation data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We visualized mutation data by "maftools" package. We calculated TMB of each patient and investigated its association with lymph node metastasis. BC patients were divided into lymph node positive and negative groups and we respectively identified TMB-related and lymph node-related differentially expressed genes (DEGs) to figure out intersected genes. Functional enrichment analysis and protein-protein interaction (PPI) network were performed to observe relevant biological functions. We constructed a TMB-related signature for predicting lymph node metastasis through Logistic regression analysis. A validation database (GSE102484) from the Gene Expression Omnibus (GEO) database was downloaded to verify the accuracy.

Results: Single nucleotide polymorphism (SNP) occupied the highest proportion in variant types while C>T appeared most frequently in single nucleotide variant (SNV). TMB was regarded as negatively correlated with lymph node metastasis in BC (P=0.003). We identified 125 common DEGs through venn diagram, which were enriched in vesicle localization, calcium signaling pathway and salmonella infection. A TMB-related signature based on six genes (BAHD1, PPM1A, PQLC3, SMPD3, EEF1A1 and S100B) had reliable efficacy for predicting lymph node metastasis in BC and was proven as an independent predictive factor. The accuracy of this signature was further validated by GSE102484 database.

Conclusions: Our results indicated that TMB was associated with lymph node metastasis of BC. We built a TMB-related signature consisting of six genes which might function as a novel biomarker for predicting lymph node metastasis in BC.

Keywords: Breast cancer (BC); bioinformatics; lymph node metastasis; predictive signature; tumor mutation burden (TMB).